Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. Generalities of Artificial Intelligence
3.1.1. Machine Learning
3.1.2. Deep Learning
3.2. Bladder Cancer Diagnosis
3.2.1. Bladder Tumor Detection through Cystoscopy
3.2.2. Bladder Tumor Detection through Urine Cytology
3.2.3. Bladder Tumor Detection through Urine Metabolomes
3.2.4. Bladder Cancer Segmentation Research
3.2.5. Bladder Cancer Imaging and Artificial Intelligence
3.2.6. Bladder Cancer Grading and Artificial Intelligence
3.2.7. Bladder Cancer and Histopathology
3.2.8. Bladder Cancer Staging and Artificial Intelligence
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Term | Brief Explanation |
---|---|
Perceptron | a super-simplified version of a biological neuron, which takes different inputs and weighs them up to produce a single output [15] |
Backpropagation | an algorithm that is used to train neural networks [15] |
Artificial Neural Networks (ANN) | a computational model (i.e., algorithms or physical hardware) which mimics the human brain to process data and create patterns for decision-making [17] |
Convolutional Neural Networks (CNN) | a neural network utilizing numerous identical copies of the same neuron, thus allowing a network to learn a neuron once and use it in several places. It is particularly useful for digitized images and pattern recognition [29] |
Recurrent Neural Network (RNN) | a neural network utilizing sequential information, thus relying on previous computations [29] |
Supervised Neural Network | a neural network for which, to produce an ideal output, a prior provided output is required. It is ‘trained’ on a given pre-defined dataset and provides outputs depending on the input it has received [30] |
Unsupervised Neural Network | a neural network for which no labels are required. This involves giving a program with an unlabeled data set (i.e., that it has not been previously trained for). It is used to discover patterns and trends by clustering. [30] |
Authors/Year | INPUT/ N of Patients | AI Algorithm/ Models | OUTPUT | Summary | Performance |
---|---|---|---|---|---|
Smith et al., 2001 [73] | Gene expression Training: 156 pt Validation: 185 pt | WNN | Histopathology: pN stage | Using WNN to develop a gene expression model to predict pathological node status | AUC = 0.67 |
Seiler et al., 2016 [74] | Gene expression Training: 133 pt Validation: 66 pt | k-NN | Histopathology: pN stage | Using k-NN to develop a gene classifier to predict pathological lymph node metastasis in MIBC | AUC = 0.82 |
Cha et al., 2016 [53] | CT Urography Training: 81 pt Validation: 92 pt | CNN | Segmentation | Using CNN to segment bladder and ROIs | JSC: 0.76 |
Xu et al., 2017 [64] | T2w MRI images 62 cancer lesions, 62 controls | SVM | Histopathology: Presence of Cancer | Extracting Radiomics feature to differentiate cancer and non-cancer areas | AUC: 0.94 |
Shao et al., 2017 [50] | Urine metabolomes 87 BCa pt, 65 control | DT | Histopathology: Presence of Cancer | Evaluate urine metabolite associated with BCa | AUC: 0.77 |
Zhang et al., 2017 [70] | MRI radiomics features 61 pt | SVM | Histopathology: Grading | Using SVM to discriminate low grade and high grade bladder Ca on MRI | AUC: 0.86 |
Garapati et al., 2017 [78] | CT images texture analysis 76 pt | LDA CNN SVM RF | Histopathology: Staging | Comparing 4 AI algorithms to discriminate bladder Ca < T2 and ≥T2 | AUC: 0.89–0.97 |
Vaickus et al., 2018 [45] | Urine cytology 51 negative, 60 atypical, 52 suspicious, and 54 positive cases | CNN (AlexNet/ResNet) | Citology: Detection | A hybrid deep-learning and morphometric algorithm to automate the PARIS system | ACC: >95% |
Eminaga et al., 2018 [33] | Cystoscopy images | CNN | Cistoscopy: Detection | Detect cancerous features from cystoscopy images using CNN models | ACC: 0.99 |
Khosravi et al., 2018 [47] | IHC digital slides | CNN | Histopathology: Detection | differentiate 4 biomarkers of BCa on IHC | ACC: 0.99 |
Sokolov et al., 2018 [48] | High resolutions images using atomic force microscopy. 25 cancer lesions, 43 control | ML | Histopathology: Detection | Non-invasive detection of BCa | ACC: 0.94 |
Wu et al., 2018 [65] | T2w MRI images Training: 69 pt Validation: 34 pt | LASSO, LR | Histopathology: pN stage | Building a nomogram with mpMRI radiomic features | AUC: 0.84 |
Wu et al., 2018 [75] | Gene expression Training: 178 pt Validation: 246 pt | LR | Histopathology: pN stage | Utilizing LR to develop a genomic clinicopathologic nomogram for predicting LN metastasis | AUC: 0.89 |
Dolz et al., 2018 [54] | MRI images Training: 60 pt | CNN | Segmentation | Inner, outer wall, and tumor region segmentation | DSC: 0.69 |
Shkolyar et al., 2019 [39] | Cystoscopy images Training: 95 pt | CNN | Cistoscopy: Detection | Using “Cystonet” a CNN to discriminate malignant from benign images | SENS: 91% SPEC: 99% |
Zheng et al., 2019 [66] | T2w MRI images Training: 130 pt Validation: 69 pt | LASSO, LR | Histopathology: pT stage | Building a nomogram with mpMRI radiomic features | AUC: 0.88 |
Wang et al., 2019 [71] | T2w MRI images Training: 70 pt Validation: 30 pt | LR | Histopathology: Grading | Utilizing MRI radiomics features to discriminate low and high-grade BCa | AUC: 88.2 |
Zhang et al., 2019 [76] | Histopathology digital images Training: 620 Validation: 193 | CNN | Histopathology: pT stage | Utilizing CNN to analyze bladder Ca WSI compared to expert histopathologists | AUC: 0.97 |
Sanghvi et al., 2019 [46] | Urine cytology Training: 2405 urine sample Prospective Validation | CNN | Cistoscopy: Detection | Artificial Intelligence Algorithm for Reporting Urine Cytopathology | AUC: 0.88 |
Kouznetsova et al., 2019 [51] | Urine metabolomes | ANN, LR | Histopathology: pT stage | Recognition of Early and Late Stages of Bladder Cancer Using Metabolites and Machine Learning | ACC: 0.82 |
Ma et al., 2019 [57] | CT Urography Training: 81 pt Validation: 92 pt | U-net DCNN | Segmentation | Deep Learning Bladder Segmentation in CT Urography | JSC: 0.85 |
Xu et al., 2019 [79] | T2w and DWI MRI images Training: 54 pt | SVM | Histopathology: pT stage | BCa staging with MRI Radiomics Analysis | AUC:0.97 |
Ikeda et al., 2020 [36] | 2102 Cystoscopy images | CNN | Cistoscopy: Detection | Development of a Support System for Cystoscopic Diagnosis of BCa | AUC: 0.98 |
Lorencin et al., 2020 [34] | 2983 Cystoscopy images | ANN | Cistoscopy: Detection | Development of a Support System for Cystoscopic Diagnosis of BCa | AUC: 0.99 |
Li et al., 2020 [55] | MRI 1092 pt | U-net | Segmentation | Deep Learning Bladder Segmentation in MRI images | DSC: 0.85 |
Niazi et al., 2020 [56] | Histopathology digital images of pT1 pt | U-net | Segmentation | Deep Learning for bladder layers identification on Pathology images | ACC: 0.90 |
Yin et al., 2020 [80] | Histopathology digital images of pTa and pT1 pt | SVM, LR, RF, ANN | Histopathology: pT stage | Histopathological staging of BCa using different ML Approaches | ACC: 0.96 |
Jansen et al., 2020 [72] | Histopathology digital images | U-net | Histopathology: Grading | Detection and grading of BCa | ACC: 0.76 |
Lorencin et al., 2021 [35] | 2983 Cystoscopy images | CNN | Cystoscopy: Detection | Development of a Support System for Cystoscopic Diagnosis of BCa | AUC:0.99 |
Nojima et al., 2021 [43] | Urine cytology | 16-layer Visual Geometry Group CNN | Detection and Grading | DL diagnosis and grading of BCa using urine Cytology | AUC: 0.98, F1 score: 0.90 (Presence/Absence) AUC: 0.86, F1 score: 0.82 (Invasive/non invasive) AUC: 0.86, F1 score: 0.82 (low-grade/high-grade |
Yang et al., 2021 [37] | Cystoscopy images | CNN | Cystoscopy: Detection | Comparisons of a Support Systems for Cystoscopic Diagnosis of BCa | ACC: 0.97 |
Awan et al., 2021 [44] | Urine cytology | CNN | Detection | Identification of atypic cells | AUC: 0.99 |
Yang et al. (2021b) [81] | CT Images, 1200 images from 369 pt | CNN | Histopathology: pT stage | DL to differentiate Muscle-Invasive BCa with CT | AUC: 0.99 |
Lilli et al., 2021 [49] | Urine cytology | CNN | Detection | Identification of Cancer cells | ACC: 89.90% |
Du et al., 2021 [38] | Cystoscopy images 1736 pt | CNN EasyDL Caffe DL | Cystoscopy: Detection | Comparisons of a Support Systems for Cystoscopic Diagnosis of BCa | ACC = 82.9% (Caffe DL) ACC = 96.9% (EasyDL) |
Taguchi et al., 2021 [68] | MRI images 68 pt | CNN | Detection | VI-RADS score and DL for BCa detection | AUC: 0.92 |
Velmahos et al., 2021 [77] | Histopathology digital images | CNN | Histopathology: FGFR alterations and tumor-infiltrating lymphocytes | Deep Learning to Identify Bladder Cancers with FGFR-Activating Mutations | AUC: 0.86 |
Ali et al., 2021 [41] | Blue light cystoscopy images | CNN | Cystoscopy: Detection Histopathology: Staging | Blue-light cystoscopy and CNN algorithm to detect, grade, and stage BCa | Detect-SENS = 95.77% SPEC = 87.84% Staging-SENS = 88% SPEC = 96.56% |
Yoo et al., 2022 [42] | Cystoscopy images | SVM | Cystoscopy: Detection | Cystoscopic Diagnosis of BCa using a red-green-blue method. | SENS = 95.0% SPEC = 93.7% DSC = 74.7% |
Wu et al., 2022 [40] | Cystoscopy images | CNN (ResNet) | Cystoscopy: Detection | Support Systems for Cystoscopic Diagnosis of BCa | ACC = 93.9%, SENS = 95.4% |
Xu et al. [84] 2022 | CT images 60 pt | CNN YOLO | Histopathology: pT stage | Predicting pT stage at pre-operative CT scan | CR: T1 stage = 50.01% T2a = 91.65%, T2b, T3 and T4 stage = 100.00% |
Zou et al., 2022 [85] | T2w MRI images Prospective cohort | CNN Inception V3 | Histopathology: pT stage | CNN to extract features and build a model predicting pT stage | ACC = 92.3% SENS = 100% SPEC = 88.5% |
Zhang et al., 2023 [58] | Cystoscopy images | U-Net | Segmentation | Deep Learning Tumor Segmentation during cystoscopy | Dice = 82.7% MioU = 69% |
Li et al., 2023 [82] | T2w MRI images | CNN LASSO SVM | Histopathology: pT stage | Accuracy of radiomics, single- and multi-task DL on T2w MRI images for staging | Radiomics-AUC = 0.920 Singletask = AUC = 0.933 Multitask = AUC = 0.932 |
Sarkar et al., 2023 [69] | CT | Hybrid ML and DL | Histopathology: Detection Staging | Hybrid ML and DL model to automatically detect and stage BCa | Detection: ACC = 86.07% Staging: ACC = 79.72% |
Li et al., 2023 [83] | T2w MRI images | CNN VI-RADS | Staging | DL-CNN model based on T2w vs. VI-RADS in BCa staging | (CNN) AUC = 0.963 (VIRADS) AUC = 0.84 |
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Ferro, M.; Falagario, U.G.; Barone, B.; Maggi, M.; Crocetto, F.; Busetto, G.M.; Giudice, F.d.; Terracciano, D.; Lucarelli, G.; Lasorsa, F.; et al. Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement. Diagnostics 2023, 13, 2308. https://doi.org/10.3390/diagnostics13132308
Ferro M, Falagario UG, Barone B, Maggi M, Crocetto F, Busetto GM, Giudice Fd, Terracciano D, Lucarelli G, Lasorsa F, et al. Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement. Diagnostics. 2023; 13(13):2308. https://doi.org/10.3390/diagnostics13132308
Chicago/Turabian StyleFerro, Matteo, Ugo Giovanni Falagario, Biagio Barone, Martina Maggi, Felice Crocetto, Gian Maria Busetto, Francesco del Giudice, Daniela Terracciano, Giuseppe Lucarelli, Francesco Lasorsa, and et al. 2023. "Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement" Diagnostics 13, no. 13: 2308. https://doi.org/10.3390/diagnostics13132308
APA StyleFerro, M., Falagario, U. G., Barone, B., Maggi, M., Crocetto, F., Busetto, G. M., Giudice, F. d., Terracciano, D., Lucarelli, G., Lasorsa, F., Catellani, M., Brescia, A., Mistretta, F. A., Luzzago, S., Piccinelli, M. L., Vartolomei, M. D., Jereczek-Fossa, B. A., Musi, G., Montanari, E., ... Tataru, O. S. (2023). Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement. Diagnostics, 13(13), 2308. https://doi.org/10.3390/diagnostics13132308